Shuang Li Assistant Professor and Presidential Young Fellow School of Data Science The Chinese University of Hong Kong (Shenzhen) Daoyuan Building, 508b Shenzhen, Guangdong, China Email: lishuang@cuhk.edu.cn

Biography

I am currently an Assistant Professor in the School of Data Science at The Chinese University of Hong Kong (Shenzhen) . I was a postdoctoral fellow with the Department of Statistics at Harvard University from Sep, 2019 to May, 2021. I was very fortunate to work with Prof. Susan Murphy in mobile health. I obtained my Ph.D. in Industrial Engineering (specialization in Statistics, minor in Operations Research) from H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech in summer 2019. I received B.E. in Automation from University of Science and Technology, China in 2011, and M.S. in Statistics from Georgia Tech in 2014.

I interned at Google in summer 2018, working on deep learning for user behavior modeling.

[Google Scholar] [Curriculum Vitae]

I am now recruiting Ph.d. students (2023), research assistants, and postdocs. I still have two Ph.d. opennings. If you are interested in my Ph.D. position starting in Fall 2023, you can contact me via email.

Research Interests

My research has yielded new sequential data analysis and decision-making tools, all inspired and motivated by applications in healthcare, smart cities and social media. I develop new methodological frameworks that combine deep learning, time series analysis and point processes to address the complexity and volume of the sequential data collected in modern systems.

More specifically, my research focuses on:

• Novel models to capture complex dynamics in sequences

• Reliable and efficient learning methods to uncover latent model parameters

• Effective inference procedures based on these models to perform accurate prediction, reliable detection, and smart interventions

Highlights of my research can be found here.

News

Feb, 2023, I was invited to serve as an area chair at NeurIPs, 2023.

Jan, 2022, our paper "Explaining Point Processes by Learning Interpretable Temporal Logic Rules" was accepted by ICLR, 2022.

Dec, 2021, I was invited to serve as a meta reviewer (i.e., area chair) at ICML, 2022.

June, 2021, I became a tenure-track Assistant Professor with the School of Data Science at The Chinese University of Hong Kong (Shenzhen).

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Awards

• Presidential Young Fellow
  The Chinese University of Hong Kong, Shenzhen. Aug 2021- 2023

• Finalist, QSR Best Student Paper Competition
  INFORMS, 2018

• Finalist, Social Media Analytics Best Student Paper Competition
  INFORMS, 2018

• Second Place, Jarvis Award for Graduate Student Research in ISyE
  Georgia Tech, 2016

Publications

Conference

• Explaining Point Processes by Learning Interpretable Temporal Logic Rules
  S. Li*, M. Feng *, L. Wang, A. Essofi, Y. Cao, J, Yan and L. Song.
  International Conference on Learning Representations (ICLR), 2022.

• Temporal Logic Point Processes
  S. Li, L. Wang, R. Zhang, X. Chang, X. Liu, Y. Xie, Y. Qi, and L. Song
  International Conference on Machine Learning (ICML), 2020.

• Continuous-Time Dynamic Graph Learning via Neural Interaction Processes
  X. Chang, X. Liu, J. Wen, S. Li, Y, Fang, L. Song, and Y. Qi
  The Conference on Information and Knowledge Management (CIKM), 2020.

• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
  X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song
  International Conference on Machine Learning (ICML), 2019.

• Learning Temporal Point Processes via Reinforcement Learning
  S. Li, S. Xiao, S. Zhu, N. Du, Y. Xie, and L. Song
  Neural Information Processing Systems (NIPS), 2018. (Spotlight, acceptance rate: 168/4856 = 3.5%)

• Recurrent Hidden Semi-Markov Model
  H. Dai, B. Dai, Y. Zhang, S. Li, and L. Song
  International Conference on Learning Representations (ICLR), 2017.

• Fake News Mitigation via Point Processes Based Intervention
  M. Farajtabar, J. Yang, X. Ye, R. Trivedi, E. Khalil, S. Li, H. Xu, L. Song, and H. Zha
  International Conference on Machine Learning (ICML), 2017.

• Online Seismic Event Picking Via Sequential Change-Point Detection
  S. Li, Y. Cao, C. Leamon, Y. Xie, L. Shi, and W. Song
  Allerton Conference on Control, Communications and Computing (Allerton), 2016.

• M-Statistic for Kernel Change-Point Detection
  S. Li, Y. Xie, H. Dai, and L. Song
  Neural Information Processing Systems (NIPS), 2015.

• Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
  Y. Liu, S. Li, F. Li, L. Song, and J. Rehg
  Neural Information Processing Systems (NIPS), 2015.

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song
  Neural Information Processing Systems (NIPS), 2015. (Oral, acceptance rate: 15/1838 = 0.8%)

Journal

• Micro-Randomized Trials for Promoting Engagement in Mobile Health Data Collection: Adolescent/Young Adult Oral Chemotherapy Adherence as an Example
  S. Li, A. Psihogios, E. McKelvey, A. Ahmed, M. Rabbi, and S. Murphy
  Current Opinion in Systems Biology, in press, 2020+.

• Scan B-statistic for Kernel Change-point Detection
  S. Li, Y. Xie, H. Dai, and L. Song
  Sequential Analysis, 38(4):503-544, 2019.
         — Finalist of 2018 INFORMS QSR Best Student Paper Competition

• Detecting Weak Changes in Dynamic Events over Networks
  S. Li, Y. Xie, M. Farajtabar, A. Verma, and L. Song
  IEEE Transactions on Signal and Information Processing over Networks, Vol. 3, No. 2, June 2017.
         — Finalist of 2018 INFORMS Social Media analytics Best Student Paper Competition

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song
  Journal of Machine Learning Research, 2017.

• Control for Time-Varying Delay Systems by Integrating Semi-Discretization and Hysteresis-Based Switching
  C. Shao, S. Li, H. Li, and J. Sheng
  Asian Journal of Control, 2018.

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song
  The Web Conference, Journal Track, 2018.

• Reinforcement Learning of Spatio-Temporal Point Processes
  S. Zhu, S. Li, Z. Peng, and Y. Xie.
  submitted to IEEE Transactions on Knowledge and Data Engineering.

Book Chapter

• Learning Continuous-Time Hidden Markov Models for Event Data
  Y. Liu, A. Moreno, S. Li, F. Li, L. Song, and J. Rehg.
  Mobile Health, Springer, 2017.

Workshop

• Interpretable Deep Generative Spatio-Temporal Point Processes
  S. Zhu, S. Li, Z. Peng, and Y. Xie
  NeurIPS Workshop on AI for Earth Sciences, 2020.

• Temporal Logic Point Processes Processes
  S. Li, L. Wang, R. Zhang, Y. Xie, N. Du, and L. Song
  NeurIPS Workshop on Learning with Temporal Point Processes, 2019. (Oral)

• Co-evolutionary Dynamics of Information Diffusion and Network Structure
  M. Farajtabar, M. Gomez-Rodriguez, Y. Wang, S. Li, H. Zha, and L. Song
  Workshop on Activity and Events in Networks: Models, Methods Applications, in conjunction with International World Wide Web Conference (WWW) 2015

Teaching

• DDA 6060 Machine Learning
         — Graduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2022.
         — Instructor

• DDA 2001 Introduction to Data Science
         — Undergraduate level course, The Chinese University of Hong Kong, Shenzhen, Fall 2021.
         — Instructor

• STAT 234 Sequential Decision Making
         — Graduate level course for students in Statistics and Computer Science, Harvard University, Spring 2021.
         — Prepared course materials

• CSE/ISYE 6740 Computational Data Analysis/CS 7641 Machine Learning
         — Graduate level course in Machine Learning, Georgia Tech, Spring 2019, Spring 2018, Fall 2016, Fall 2014.
         — Teaching Assistant

• CX 4240 Introduction to Computational Data Analysis
         — Undergraduate level course in Machine Learning, Georgia Tech, Spring 2017, Spring 2016.
         — Teaching Assistant

Services

• ICML, NIPS, AAAI, AISTATS, WWW, UAI, ICASSP

• Journal of American Statistical Association, Annals of Applied Statistics, IEEE Transactions on Signal Processing, IEEE Transactions on Information Theory, Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems